Transcriptional Shift Identifies a Set of Genes Driving Breast Cancer Chemoresistance
MetadataShow full item record
AuthorVera Ramírez, Laura; Sánchez Rovira, Pedro; Ramírez Tortosa, César Luis; Quiles Morales, José Luis; Ramírez Tortosa, María del Carmen; Lorente Acosta, José Antonio
Public Library of Science (PLOS)
Breast cancerCancer stem cellsCancer treatmentChemotherapyGene expressionGene targetingGenetic networksMicroarrays
Vera-Ramírez, L.; et al. Transcriptional Shift Identifies a Set of Genes Driving Breast Cancer Chemoresistance. Plos One, 8(1): e53983 (2013). [http://hdl.handle.net/10481/30790]
SponsorshipThanks are due to the Consejería de Economia, Innovación y Ciencia (CEIC) from the Junta de Andalucía and Fondo Europeo de Desarrollo Regional (FEDER)/Fondo de Cohesión Europeo (FSE) to financial support through the Programa Operativo FEDER/FSE de Andalucía 2007-2013 and the research project CTS-5350. The authors also acknowledge financial support by the PN de I+D+i 2006-2009/ISCIII/Ministerio de Sanidad, Servicios Sociales e Igualdad (Spain) and Fondo Europeo de Desarrollo Regional (FEDER) from the European Union, through the research project PI06/90388.
Background Distant recurrences after antineoplastic treatment remain a serious problem for breast cancer clinical management, which threats patients’ life. Systemic therapy is administered to eradicate cancer cells from the organism, both at the site of the primary tumor and at any other potential location. Despite this intervention, a significant proportion of breast cancer patients relapse even many years after their primary tumor has been successfully treated according to current clinical standards, evidencing the existence of a chemoresistant cell subpopulation originating from the primary tumor.Methods/Findings To identify key molecules and signaling pathways which drive breast cancer chemoresistance we performed gene expression analysis before and after anthracycline and taxane-based chemotherapy and compared the results between different histopathological response groups (good-, mid- and bad-response), established according to the Miller & Payne grading system. Two cohorts of 33 and 73 breast cancer patients receiving neoadjuvant chemotherapy were recruited for whole-genome expression analysis and validation assay, respectively. Identified genes were subjected to a bioinformatic analysis in order to ascertain the molecular function of the proteins they encode and the signaling in which they participate. High throughput technologies identified 65 gene sequences which were over-expressed in all groups (P ≤ 0·05 Bonferroni test). Notably we found that, after chemotherapy, a significant proportion of these genes were over-expressed in the good responders group, making their tumors indistinguishable from those of the bad responders in their expression profile (P ≤ 0.05 Benjamini-Hochgerg`s method).Conclusions These data identify a set of key molecular pathways selectively up-regulated in post-chemotherapy cancer cells, which may become appropriate targets for the development of future directed therapies against breast cancer.